Plotly：如何自定义图例顺序？

Question

我在 Python 中绘制了 scatter_geo 图。

import plotly.express as px
import pandas as pd

rows=[['501-600','65','122.58333','45.36667'],
      ['till 500','54','12.5','27.5'],
      ['more 1001','51','-115.53333','38.08'],
      ['601-1000','54','120.54167','21.98'],
      ]

colmns=['bins','data','longitude','latitude']
df=pd.DataFrame(data=rows, columns=colmns)

fig=px.scatter_geo(df,lon='longitude', lat='latitude',color='bins',
                      opacity=0.5,
                      projection="natural earth")
fig.show()

如果我只有一条数据痕迹，是否有可能在图例标签中自定义顺序？

因为现在图例中的标签如下所示：

501-600
till 500
more 1001
601-1000

我需要让它们看起来像这样：

till 500
501-600
601-1000
more 1001

Answer 1

If you take a look at traceorder you'll see that items are displayed top-to-bottom in the same order as the input data for the "normal" option. So you can just change the order of your input data to get what you want:

And if you'd like to specify an arbitrary order, you can do so by defining the order in the input data. Specifying row order can ba a bit tedious, so I often transpose the dataframe, order the categories that now appear in the column, and transpose it right back to its original form:

order  = ['till 500', '501-600', '601-1000', 'more 1001']
df_ordered = df.T[order].T

With this order, the result will be the same as the figure above. Here's an updated code snippet:

Complete code:

import plotly.express as px
import pandas as pd

rows=[['501-600','65','122.58333','45.36667'],
      ['till 500','54','12.5','27.5'],
      ['more 1001','51','-115.53333','38.08'],
      ['601-1000','54','120.54167','21.98'],
      ]

colmns=['bins','data','longitude','latitude']
df=pd.DataFrame(data=rows, columns=colmns)

fig=px.scatter_geo(df,lon='longitude', lat='latitude',color='bins',
                      opacity=0.5,
                      projection="natural earth")
fig.show()


order  = ['till 500', '501-600', '601-1000', 'more 1001']
df = df.set_index('bins')
df_ordered = df.T[order].T.reset_index()
df_ordered

fig2=px.scatter_geo(df_ordered,lon='longitude', lat='latitude',color='bins',
                      opacity=0.5,
                      projection="natural earth")
fig2.show()